1. Improved Short-Term Speed Prediction Using Spatiotemporal-Vision-Based Deep Neural Network for Intelligent Fuel Cell Vehicles
- Author
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Zhiyu Huang, Chenghao Deng, Caizhi Zhang, Chen Lv, Jinrui Chen, Dong Hao, Yuanzhi Zhang, Hongxu Ran, and School of Mechanical and Aerospace Engineering
- Subjects
Neural Networks ,Vision based ,Artificial neural network ,Computer science ,Energy management ,Attenuation ,020208 electrical & electronic engineering ,Real-time computing ,02 engineering and technology ,Energy consumption ,Motion (physics) ,Computer Science Applications ,Term (time) ,Lithium-Ion Batteries ,Control and Systems Engineering ,Mechanical engineering [Engineering] ,0202 electrical engineering, electronic engineering, information engineering ,Fuel cells ,Electrical and Electronic Engineering ,Information Systems - Abstract
In this article, an improved short-term speed prediction method is proposed to predict short-term future speed and analyze future energy consumption of intelligent fuel cell vehicles. The short-term future speed is predicted by the proposed Inflated 3-D Inception long short-term memory (LSTM) network, which takes the spatiotemporal-vision information and vehicle motion states. Specifically, the spatiotemporal-vision-based deep neural network utilizes image sequences captured by a front-facing camera as environmental information and historical speed series as motion information to improve the prediction accuracy. Then, a case study of the proposed speed prediction method, with rule-based energy management strategy to calculate future energy consumption, is presented. The simulation results show that short-term speed prediction based on the Inflated 3-D Inception LSTM network can achieve high accuracy of speed prediction in various traffic densities, as well as low prediction errors of future energy consumption including the hydrogen consumption and state-of-charge attenuation. This work was supported in part by the National Key Research and Development Program under Grant 2018YFB0105402 and Grant 2018YFB0105703, in part by the Fundamental Research Funds for the Central Universities under Grant 2019CDXYQC0003, Grant 244005202014, 2019, and Grant 2018CDXYTW0031, in part by the National Natural Science Foundation of China under Grant 51806024, in part by the Chongqing Research Program of Foundation and Advanced Technology under Grant cstc2017jcyjAX0276, and in part by the Venture and Innovation Support Program for Chongqing Overseas Returnees under Grant cx2018051.
- Published
- 2021
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